StellarNet 🌟

StellarNet: An AI system probing the possibility that stars may possess primitive forms of information processing. By analyzing complex patterns in stellar emissions using deep learning, we search for signatures of self-organization and structured behavior that transcend random processes.

Overview

This project implements a comprehensive analysis pipeline for investigating potential "consciousness-like" patterns in stellar data using PyTorch and astronomical data from TESS and Kepler missions.

Features

• 🔬 Real-time analysis of stellar light curves from TESS/Kepler missions
• 🧠 LSTM-based pattern detection for stellar behavior prediction
• 📊 Comprehensive entropy and frequency analysis
• 🔍 Anomaly detection in stellar emissions
• 📈 Advanced visualization of stellar patterns

Installation

# Clone the repository
git clone https://github.com/Agora-Lab-AI/StellarNet.git
cd StellarNet

# Create and activate a virtual environment (optional but recommended)
python -m venv venv
source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

# Install dependencies
pip install -r requirements.txt

Quick Start

python main.py

By default, the script analyzes a set of pre-selected variable stars. To analyze specific stars:

python main.py --star_id "TIC 260128333" --mission "TESS"

Requirements

• Python 3.10+
• PyTorch
• lightkurve
• astropy
• numpy
• pandas
• scipy
• scikit-learn
• matplotlib

See requirements.txt for complete list.

Methodology

Our analysis pipeline consists of several key components:

1. Data Collection: Automated fetching of stellar light curves from TESS/Kepler missions
2. Preprocessing: Cleaning and normalization of time-series data
3. Pattern Analysis:
   • Shannon entropy calculation
   • Fourier analysis
   • LSTM-based pattern prediction
   • Anomaly detection
4. Visualization: Comprehensive plotting of results

Results

Analysis results are saved in the results/ directory with the following structure:

• {star_id}_analysis.npz: Numerical results and statistics
• {star_id}_plots.png: Visualization plots
• models/{star_id}_model.pt: Trained LSTM model

Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Open a Pull Request

Citation

If you use this code in your research, please cite:

@article{stellarnet2024,
  title={StellarNet: Investigating Information Processing Patterns in Stellar Emissions},
  author={Agora Lab AI, Kye Gomez},
  journal={arXiv preprint arXiv:2024.xxxxx},
  year={2024}
}

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• NASA's TESS and Kepler missions for providing stellar data
• The lightkurve team for their excellent data access tools
• The astropy community for their comprehensive astronomy tools

Contact

• Website: https://agoralab.ai
• Issues: GitHub Issues
• Twitter: @kyegomez
• Email: [email protected]

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